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<p>Energy management systems (EMS) are also necessary in grid-tied microgrids (MGs) in order to maximize the balance between energy generation, storage, and consumption. This balance enables the MG to operate effectively and dependably while maintaining a smooth connection with the main power grid. Integrating multiple energy sources and managing communication between distributed components increases initial setup costs and technical challenges. To overcome these challenges, this paper proposes a hybrid approach for the EMS of grid-tied MG. The proposed method combines the Kepler Optimization Algorithm (KOA) and Motif-Based Heterogeneous Graph Attention Network (MHGAN), known as the KOA–MHGAN technique. The aim is to maximize efficiency, lessen operational costs, and enhance grid stability, contributing to the overall resilience and sustainability of the energy network. The KOA optimizes energy resource scheduling and dispatch to balance supply from renewable sources and storage systems. MHGAN predicts energy flow and consumption patterns in grid-tied MGs by modeling the complex interactions between energy sources, storage systems, and loads, allowing accurate forecasting and optimization of EM decisions. The MATLAB-based implementation evaluates the performance of the proposed approach against several existing techniques, such as TLBO, PSO, SO, and SSA. The results demonstrate that KOA–MHGAN achieves superior cost and efficiency performance, with a minimum cost of 52.535 cents and a maximum efficiency of 98.6%. This improvement in cost and performance underscores the capability of KOA–MHGAN to enhance operational efficiency and strengthen the economic viability of MG operations.</p>

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A hybrid approach for energy management systems in grid tied microgrids using Kepler optimization algorithm and motif-based heterogeneous graph attention network

  • 期刊论文

  • 发表日期：

    2026年3月24日

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  • Ashutosh Pandey,
  • Sudheer Hanumanthakari,
  • B. Anandaraj,
  • Jarupula Somlal

摘要

Energy management systems (EMS) are also necessary in grid-tied microgrids (MGs) in order to maximize the balance between energy generation, storage, and consumption. This balance enables the MG to operate effectively and dependably while maintaining a smooth connection with the main power grid. Integrating multiple energy sources and managing communication between distributed components increases initial setup costs and technical challenges. To overcome these challenges, this paper proposes a hybrid approach for the EMS of grid-tied MG. The proposed method combines the Kepler Optimization Algorithm (KOA) and Motif-Based Heterogeneous Graph Attention Network (MHGAN), known as the KOA–MHGAN technique. The aim is to maximize efficiency, lessen operational costs, and enhance grid stability, contributing to the overall resilience and sustainability of the energy network. The KOA optimizes energy resource scheduling and dispatch to balance supply from renewable sources and storage systems. MHGAN predicts energy flow and consumption patterns in grid-tied MGs by modeling the complex interactions between energy sources, storage systems, and loads, allowing accurate forecasting and optimization of EM decisions. The MATLAB-based implementation evaluates the performance of the proposed approach against several existing techniques, such as TLBO, PSO, SO, and SSA. The results demonstrate that KOA–MHGAN achieves superior cost and efficiency performance, with a minimum cost of 52.535 cents and a maximum efficiency of 98.6%. This improvement in cost and performance underscores the capability of KOA–MHGAN to enhance operational efficiency and strengthen the economic viability of MG operations.